Channel pruning targets at removing the specific channels in each layer and shrink a CNN model into thinner one [1]}.
As a type of structured simplification, the advantage of channel-level pruning is removing the entire channels directly, which can be well supported by general-purpose hardware and easily leverage high-efficiency Basic Linear Algebra Subprograms (BLAS) libraries [2]}.
